loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Alana Oliveira 1 ; Mario Meireles Teixeira 2 and Carlos S. S.oares Neto 2

Affiliations: 1 Computer Engineering, Federal University of Maranhao (UFMA), Brazil, PhD Program in Computer Science, DCCMAPI / UFMA, Brazil ; 2 Department of Informatics, Federal University of Maranhao (UFMA), Brazil

Keyword(s): Learning Analytics, Educational Data Mining, Content Recommendation, Learning Styles.

Abstract: Virtual learning environments are a powerful tool in the teaching-learning process and can provide a variety of utilization data that can be explored by data mining techniques to improve the understanding of student behavior and performance. By using Learning Analytics, it is possible to identify potential problems, such as student dropout or failures before they become irreversible, and indicate corrective actions to be taken by teachers. In this context, content recommendation plays a prominent role since choosing the proper content for a certain audience may motivate them to become more involved in the learning process. However, in distance education settings nowadays, teachers do not know their students, thus it becomes difficult to select the content most suitable to their needs. In this paper, we propose a content recommendation architecture that takes into account the learning profile of students enrolled in an LMS to customize content recommendations to each learner’s style. A profile assessment tool, based on the Honey-Mumford learning style taxonomy was implemented and some preliminary data obtained. We devised a recommendation scheme that considers the euclidean distance between students’ learning styles when suggesting content to be studied. Our preliminary results indicate this approach may be beneficial to improve the teaching-learning process and student performance as a whole. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.210.143.119

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Oliveira, A.; Teixeira, M. and Neto, C. (2020). Recommendation of Educational Content to Improve Student Performance: An Approach based on Learning Styles. In Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-417-6; ISSN 2184-5026, SciTePress, pages 359-365. DOI: 10.5220/0009436303590365

@conference{csedu20,
author={Alana Oliveira. and Mario Meireles Teixeira. and Carlos S. S.oares Neto.},
title={Recommendation of Educational Content to Improve Student Performance: An Approach based on Learning Styles},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2020},
pages={359-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009436303590365},
isbn={978-989-758-417-6},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Recommendation of Educational Content to Improve Student Performance: An Approach based on Learning Styles
SN - 978-989-758-417-6
IS - 2184-5026
AU - Oliveira, A.
AU - Teixeira, M.
AU - Neto, C.
PY - 2020
SP - 359
EP - 365
DO - 10.5220/0009436303590365
PB - SciTePress